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Product-oriented vs. source-oriented generalizations

Bybee (2001:126)

“Generative rules express source-oriented generalizations. That is, they act on a specific input to change it in well-defined ways into an output of a certain form. Many, if not all, schemas are product-oriented rather than source-oriented. A product-oriented schema generalizes over forms of a specific category, but does not specify how to derive that category from some other.”

Source oriented: k]sg ti]pl

Product-oriented: ‘plurals must end in ti’

Present study

Given a lexicon and a particular training paradigm what generalizations do the learners extract?

The paradigm(Bybee & Newman 1995)

The artificial languages

Two plural suffixes –i and -a

If –i attached to a velar ({k;g}), the velar changes to an alveopalatal

This is velar palatalization

Velar palatalization

The process:

k  t /_i

Productivity:

p(k  ti) / ( p(k  ti) + p(kki) )

Coding scheme:

BLUE – velar palatalization applies

RED – velar palatalization fails

Research question

Does the productivity of velar palatalization differ in the BLUE language and the RED language?

Depends on your model of grammar.

Research question

/62

Triggers velar palatalization

Does not compete with anything

Equally supported in both languages

Non-competing rules

BLUE = RED

e.g., Hale and Reiss 2008, Plag 2003

Triggers velar palatalization

/ki/ less expected in the blue language

 its absence is less notable

Constraints

BLUE < RED

Positive product-oriented generalizations

Triggers vel.pal.

Attaches –i with or without vel.pal.

BLUE > RED

Bybee & Slobin 1982, Bybee & Moder 1983, Bybee 2001

Triggers velar palatalization

Competes with

Competition stronger in red

Competing weighted rules

BLUE > RED

Albright & Hayes 2003

Iff the choice between the rules is stochastic.

Results

BLUERED

Results

Non-competing rules

Constraints

Positive product-oriented

Competing weighted rules

100%

30

*

BLUE RED

Individual subject data

Competing weighted rulesPositive product-oriented

{p;b;t;d}

{p;b;t;d}

Albright & Hayes 2003

Results

***

ANCOVA:

This correlation is significant

F(1,27)=14.23, p<.001,

while Language is not,

F(1,27)=.082, p>.5).

The predicted explanatory variable

accounts for all the variance

in velar palatalization rate

attributable to the artificial language

21

Natural language data (Kapatsinski, in press)

In Russian loanword adaptation (English  Russian on the web not in dictionary), vel.pal is fully productive before the suffixes

Natural language data (Kapatsinski, in press)

In Russian loanword adaptation (English  Russian on the web not in dictionary), vel.pal is fully productive before the suffixes

-ek, -ok

but only partially productive before

-ik, -i.

Why?

-ik, –i tend not to attach to velars

-ok, -ek mostly attach to velars

Positive product-oriented generalizations vs.competing weighted rules

Product-oriented generalizations

Competing weighted rules

Competing weighted rules

Product-oriented

The addition of tti hurts palatalization

t(33)=2.88, p=.007

Perception / rating

Perception: The addition of tti helps palatalization

{t;d}{t;dӡ}i vs.

{t;d}{t;d}i

*

Competing weighted rules

Product-oriented

Perception: The addition of tti helps palatalization

Because ratings of {t;dӡ}i increase

(t(51)=2.245, p<.05)

Competing weighted rules

Product-oriented

*

Is perception purely product-oriented? No.

Listeners know at least that singular-final velars are the changeable segments:

{k;g}  {t;dӡ}i better than {t;d}{t;dӡ}i (p<.001) in every language

{k;g} {k;g}V better than {t;dӡ}{k;g}V (p<.00001) in every language

Learners extract competing rules, which they use to derive a word from a morphologically related word (Albright & Hayes 2003)

The outcome of competition between rules is influenced by reliability or type frequency (Albright and Hayes 2003, Pierrehumbert 2006)

The choice between rules is stochastic

Learners also learn about what a typical plural sounds like (product-oriented generalizations, Bybee 2001)

Product-oriented generalizations are used more by the listener to evaluate the goodness of a paradigmatic mapping than by the speaker to form a word from a morphologically related word (perception is more liberal than production)